Synchrotron X-ray micro-CT as a validation dataset for diffusion MRI in whole mouse brain

A scalable and modular computational pipeline for axonal connectomics: automated tracing and assembly of axons across serial sections

Progress in histological methods and in microscope technology has enabled dense staining and imaging of axons over large brain volumes, but tracing axons over such volumes requires new computational tools for 3D reconstruction of data acquired from serial sections. We have developed a computational pipeline for automated tracing and volume assembly of densely stained axons imaged over serial sections, which leverages machine learning-based segmentation to enable stitching and alignment with the axon traces themselves. We validated this segmentation-driven approach to volume assembly and alignment of individual axons over centimeter-scale serial sections and show the application of the output traces for analysis of local orientation and for proofreading over aligned volumes. The pipeline is scalable, and combined with recent advances in experimental approaches, should enable new studies of mesoscale connectivity and function over the whole human brain.

Post mortem mapping of connectional anatomy for the validation of diffusion MRI

Diffusion MRI (dMRI) is a unique tool for the study of brain circuitry, as it allows us to image both the macroscopic trajectories and the microstructural properties of axon bundles in vivo. The Human Connectome Project ushered in an era of impressive advances in dMRI acquisition and analysis. As a result of these efforts, the quality of dMRI data that could be acquired in vivo improved substantially, and large collections of such data became widely available. Despite this progress, the main limitation of dMRI remains: it does not image axons directly, but only provides indirect measurements based on the diffusion of water molecules. Thus, it must be validated by methods that allow direct visualization of axons but that can only be performed in post mortem brain tissue. In this review, we discuss methods for validating the various features of connectional anatomy that are extracted from dMRI, both at the macro-scale (trajectories of axon bundles), and at micro-scale (axonal orientations and other microstructural properties). We present a range of validation tools, including anatomic tracer studies, Klingler's dissection, myelin stains, label-free optical imaging techniques, and others. We provide an overview of the basic principles of each technique, its limitations, and what it has taught us so far about the accuracy of different dMRI acquisition and analysis approaches.

Comparative hard x-ray tomography for virtual histology of zebrafish larva, human tooth cementum, and porcine nerve

Purpose: Synchrotron radiation-based tomography yields microanatomical features in human and animal tissues without physical slicing. Recent advances in instrumentation have made laboratory-based phase tomography feasible. We compared the performance of three cutting-edge laboratory systems benchmarked by synchrotron radiation-based tomography for three specimens. As an additional criterion, the user-friendliness of the three microtomography systems was considered. Approach: The three tomography systems-SkyScan 2214 (Bruker-microCT, Kontich, Belgium), Exciscope prototype (Stockholm, Sweden), and Xradia 620 Versa (Zeiss, Oberkochen, Germany)-were given 36 h to measure three medically relevant specimens, namely, zebrafish larva, archaeological human tooth, and porcine nerve. The obtained datasets were registered to the benchmark synchrotron radiation-based tomography from the same specimens and selected ones to the SkyScan 1275 and phoenix nanotom m^® laboratory systems to characterize development over the last decade. Results: Next-generation laboratory-based microtomography almost reached the quality achieved by synchrotron-radiation facilities with respect to spatial and density resolution, as indicated by the visualization of the medically relevant microanatomical features. The SkyScan 2214 system and the Exciscope prototype demonstrated the complementarity of phase information by imaging the eyes of the zebrafish larva. The 3 - μ m thin annual layers in the tooth cementum were identified using Xradia 620 Versa. Conclusions: SkyScan 2214 was the simplest system and was well-suited to visualizing the wealth of anatomical features in the zebrafish larva. Data from the Exciscope prototype with the high photon flux from the liquid metal source showed the spiral nature of the myelin sheaths in the porcine nerve. Xradia 620 Versa, with detector optics as typically installed for synchrotron tomography beamlines, enabled the three-dimensional visualization of the zebrafish larva with comparable quality to the synchrotron data and the annual layers in the tooth cementum.

Virtual histology of an entire mouse brain from formalin fixation to paraffin embedding. Part 2: Volumetric strain fields and local contrast changes

BACKGROUND

Fixation and embedding of post mortem brain tissue is a pre-requisite for both gold-standard conventional histology and X-ray virtual histology. This process alters the morphology and density of the brain microanatomy.

NEW METHOD

To quantify these changes, we employed synchrotron radiation-based hard X-ray tomography with 3 μm voxel length to visualize the same mouse brain after fixation in 4% formalin, immersion in ethanol solutions (50%, 70%, 80%, 90%, and 100%), xylene, and finally after embedding in a paraffin block. The volumetric data were non-rigidly registered to the initial formalin-fixed state to align the microanatomy within the entire mouse brain.

RESULTS

Volumetric strain fields were used to characterize local shrinkage, which was found to depend on the anatomical region and distance to external surface. X-ray contrast was altered and enhanced by preparation-induced inter-tissue density changes. The preparation step can be selected to highlight specific anatomical features. For example, fiber tract contrast is amplified in 100% ethanol.

COMPARISON WITH EXISTING METHODS

Our method provides volumetric strain fields, unlike approaches based on feature-to-feature or volume measurements. Volumetric strain fields are produced by non-rigid registration, which is less labor-intensive and observer-dependent than volume change measurements based on manual segmentations. X-ray microtomography provides spatial resolution at least an order of magnitude higher than magnetic resonance microscopy, allowing for analysis of morphology and density changes within the brain's microanatomy.

CONCLUSION

Our approach belongs to three-dimensional virtual histology with isotropic micrometer spatial resolution and therefore complements atlases based on a combination of magnetic resonance microscopy and optical micrographs of serial histological sections.

MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/.